Content based selection and meta tagging of advertisement breaks

ABSTRACT

Example methods, apparatus, systems and machine readable media are disclosed herein for analyzing response data from subjects exposed to media. An example method includes collecting electroencephalography data from a brain of a subject exposed to an advertisement. The example method also includes determining neuro-response data from the electroencephalography data, the neuro-response data representative of interaction between a first frequency band and a second frequency band of the electroencephalography data, the second frequency band being different than the first frequency band. In addition, the example method includes determining resonance to the advertisement based on the neuro-response data and identifying one or more priming levels for one or more locations in a source material based on an attribute of the source material. The example method also includes selecting a first one of the one or more locations for inclusion of the advertisement based on the resonance and the priming levels.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises as a continuation of U.S. application Ser. No. 12/200,813, which was filed on Aug. 28, 2008, and claims the benefit under 35 U.S.C. §119(e) to U.S. Provisional Application 60/968,567, which was filed on Aug. 29, 2007, both of which are hereby incorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to content based selection and meta-tagging of advertisement breaks.

BACKGROUND

Conventional systems for content selection and meta-tagging of advertisement breaks are limited or non-existent. Some conventional systems provide very rudimentary information for content selection through demographic information and statistical data. However, conventional systems are subject to semantic, syntactic, metaphorical, cultural, and interpretive errors.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the following description taken in conjunction with the accompanying drawings, which illustrate particular examples.

FIG. 1 illustrates one example of a system for content selection and meta-tagging of advertisement breaks.

FIG. 2 illustrates examples of stimulus attributes that can be included in a stimulus attributes repository.

FIG. 3 illustrates examples of data models that can be used with a stimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with the content selection and meta-tagging system.

FIG. 5 illustrates one example of a report generated using the content selection and meta-tagging system.

FIG. 6 illustrates one example of a technique for performing data analysis.

FIG. 7 illustrates one example of technique for content selection and meta-tagging of advertisement breaks.

FIG. 8 provides one example of a system that can be used to implement one or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of the disclosure including the best modes contemplated by the inventors for carrying out the teachings of the disclosure. Examples of these specific examples are illustrated in the accompanying drawings. While the disclosure is described in conjunction with these specific examples, it will be understood that it is not intended to limit the disclosure to the described examples. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the disclosure as defined by the appended claims.

For example, the techniques and mechanisms of the present disclosure will be described in the context of particular types of data such as central nervous system, autonomic nervous system, and effector data. However, it should be noted that the techniques and mechanisms of the present disclosure apply to a variety of different types of data. It should be noted that various mechanisms and techniques can be applied to any type of stimuli. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. Particular examples of the present disclosure may be implemented without some or all of these specific details. In other instances, well known process operations have not been described in detail in order not to unnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure will sometimes be described in singular form for clarity. However, it should be noted that some examples include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. For example, a system uses a processor in a variety of contexts. However, it will be appreciated that a system can use multiple processors while remaining within the scope of the present disclosure unless otherwise noted. Furthermore, the techniques and mechanisms of the present disclosure will sometimes describe a connection between two entities. It should be noted that a connection between two entities does not necessarily mean a direct, unimpeded connection, as a variety of other entities may reside between the two entities. For example, a processor may be connected to memory, but it will be appreciated that a variety of bridges and controllers may reside between the processor and memory. Consequently, a connection does not necessarily mean a direct, unimpeded connection unless otherwise noted.

OVERVIEW

Consequently, it is desirable to provide improved methods and apparatus for content selection and meta-tagging of advertisement (ad) breaks.

A system evaluates stimulus materials such as videos, imagery, web pages, text, etc., in order to determine resonance and priming levels for various products and services at different temporal and spatial locations including advertisement breaks in the stimulus materials. The stimulus materials are tagged with resonance and priming level information to allow intelligent selection of suitable advertisement content for insertion at various locations in the stimulus materials. Response data such as survey data and/or neuro-response data including Event Related Potential (ERP), Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding data may be used to determine resonance and priming levels.

Examples

Conventional mechanisms for selecting advertising content rely on demographic information, statistical information, and survey based response collection. One problem with conventional mechanisms for selecting advertising is that they do not measure the inherent message resonance and priming for various products, services, and offerings that are attributable to the stimulus. They are also prone to semantic, syntactic, metaphorical, cultural, and interpretive errors thereby preventing the accurate and repeatable targeting of the audience.

Conventional systems do not use neuro-behavioral and neuro-physiological response blended manifestations in assessing the user response and do not elicit an individual customized neuro-physiological and/or neuro-behavioral response to the stimulus. Conventional systems also fail to blend multiple datasets, and blended manifestations of multi-modal responses, across multiple datasets, individuals and modalities, to reveal and validate the elicited measures of resonance and priming to allow for intelligent selection of advertising content.

In these respects, the content selection and meta-tagging of advertising break system according to the present disclosure substantially departs from the conventional concepts and designs of the prior art. According to various examples, it is recognized that advertisements for particular products, services, and offerings may be particularly effective when a subject is primed for the particular products, services, and offerings. For examples, an advertisement for cleaning supplies may be particularly effective after viewers watch a scene showing a dirty room, or an advertisement for a fuel efficient car may be particularly effective after viewers watch a documentary about high oil prices. In still other examples, an audio advertisement for packaged salads may be more effective after viewers hear a radio program about coronary disease, or a brand image for camping products may be more effective placed near a mural showing mountain scenery.

Consequently, the techniques and mechanisms of the present disclosure tag stimulus materials such as video, audio, web pages, printed materials, etc. with information indicating resonance and/or priming levels for various products, services and offerings. Meta-tags may be stored in a separate repository or in the stimulus material itself. In some examples, advertising content suitable for particular priming levels may be automatically selected based on meta-tags for introduction into the stimulus materials. In other examples, advertising content can be intelligently inserted based on priming levels for particular products and services. In some examples, advertising break slots can be sold or auctioned more efficiently based on priming levels and resonance. Advertisers can assess the value of particular slots based on priming levels and resonance.

According to various examples, the techniques and mechanisms of the present disclosure may use a variety of mechanisms such as survey based responses, statistical data, and/or neuro-response measurements such as central nervous system, autonomic nervous system, and effector measurements to improve content selection and meta-tagging of stimulus material. Some examples of central nervous system measurement mechanisms include Functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG). fMRI measures blood oxygenation in the brain that correlates with increased neural activity. However, current implementations of FMRI have poor temporal resolution of few seconds. EEG measures electrical activity associated with post synaptic currents occurring in the milliseconds range. Subcranial EEG can measure electrical activity with the most accuracy, as the bone and dermal layers weaken transmission of a wide range of frequencies. Nonetheless, surface EEG provides a wealth of electrophysiological information if analyzed properly. Even portable EEG with dry electrodes provides a large amount of neuro-response information.

Autonomic nervous system measurement mechanisms include Galvanic Skin Response (GSR), Electrocardiograms (EKG), pupillary dilation, etc. Effector measurement mechanisms include Electrooculography (EOG), eye tracking, facial emotion encoding, reaction time etc.

According to various examples, the techniques and mechanisms of the present disclosure intelligently blend multiple modes and manifestations of precognitive neural signatures with cognitive neural signatures and post cognitive neurophysiological manifestations to more accurately perform content selection and meta-tagging. In some examples, autonomic nervous system measures are themselves used to validate central nervous system measures. Effector and behavior responses are blended and combined with other measures. According to various examples, central nervous system, autonomic nervous system, and effector system measurements are aggregated into a measurement that allows content selection and meta-tagging of advertising breaks.

In particular examples, subjects are exposed to stimulus material and data such as central nervous system, autonomic nervous system, and effector data is collected during exposure. According to various examples, data is collected in order to determine a resonance measure that aggregates multiple component measures that assess resonance data. In particular examples, specific event related potential (ERP) analyses and/or event related power spectral perturbations (ERPSPs) are evaluated for different regions of the brain both before a subject is exposed to stimulus and each time after the subject is exposed to stimulus.

Pre-stimulus and post-stimulus differential as well as target and distracter differential measurements of ERP time domain components at multiple regions of the brain are determined (DERP). Event related time-frequency analysis of the differential response to assess the attention, emotion and memory retention (DERPSPs) across multiple frequency bands including but not limited to theta, alpha, beta, gamma and high gamma is performed. In particular examples, single trial and/or averaged DERP and/or DERPSPs can be used to enhance the resonance measure and determine priming levels for various products and services.

A variety of stimulus materials such as entertainment and marketing materials, media streams, billboards, print advertisements, text streams, music, performances, sensory experiences, etc. can be analyzed. According to various examples, enhanced neuro-response data is generated using a data analyzer that performs both intra-modality measurement enhancements and cross-modality measurement enhancements. According to various examples, brain activity is measured not just to determine the regions of activity, but to determine interactions and types of interactions between various regions. The techniques and mechanisms of the present disclosure recognize that interactions between neural regions support orchestrated and organized behavior. Attention, emotion, memory, and other abilities are not merely based on one part of the brain but instead rely on network interactions between brain regions.

The techniques and mechanisms of the present disclosure further recognize that different frequency bands used for multi-regional communication can be indicative of the effectiveness of stimuli. In particular examples, evaluations are calibrated to each subject and synchronized across subjects. In particular examples, templates are created for subjects to create a baseline for measuring pre and post stimulus differentials. According to various examples, stimulus generators are intelligent and adaptively modify specific parameters such as exposure length and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillary dilation, EOG, eye tracking, facial emotion encoding, reaction time, etc. Individual modalities such as EEG are enhanced by intelligently recognizing neural region communication pathways. Cross modality analysis is enhanced using a synthesis and analytical blending of central nervous system, autonomic nervous system, and effector signatures. Synthesis and analysis by mechanisms such as time and phase shifting, correlating, and validating intra-modal determinations allow generation of a composite output characterizing the significance of various data responses to effectively perform content selection and meta-tagging.

FIG. 1 illustrates one example of a system for performing content selection and meta-tagging using central nervous system, autonomic nervous system, and/or effector measures. According to various examples, the content selection and meta-tagging system includes a stimulus presentation device 101. In particular examples, the stimulus presentation device 101 is merely a display, monitor, screen, etc., that displays stimulus material to a user. The stimulus material may be a media clip, a commercial, pages of text, a brand image, a performance, a magazine advertisement, a movie, an audio presentation, and may even involve particular tastes, smells, textures and/or sounds. The stimuli can involve a variety of senses and occur with or without human supervision. Continuous and discrete modes are supported. According to various examples, the stimulus presentation device 101 also has protocol generation capability to allow intelligent customization of stimuli provided to multiple subjects in different markets.

According to various examples, stimulus presentation device 101 could include devices such as televisions, cable consoles, computers and monitors, projection systems, display devices, speakers, tactile surfaces, etc., for presenting the stimuli including but not limited to advertising and entertainment from different networks, local networks, cable channels, syndicated sources, websites, internet content aggregators, portals, service providers, etc.

According to various examples, the subjects are connected to data collection devices 105. The data collection devices 105 may include a variety of neuro-response measurement mechanisms including neurological and neurophysiological measurements systems such as EEG, EOG, GSR, EKG, pupillary dilation, eye tracking, facial emotion encoding, and reaction time devices, etc. According to various examples, neuro-response data includes central nervous system, autonomic nervous system, and effector data. In particular examples, the data collection devices 105 include EEG 111, EOG 113, and GSR 115. In some instances, only a single data collection device is used. Data collection may proceed with or without human supervision.

The data collection device 105 collects neuro-response data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (GSR, EKG, pupillary dilation), and effector sources (EOG, eye tracking, facial emotion encoding, reaction time). In particular examples, data collected is digitally sampled and stored for later analysis. In particular examples, the data collected could be analyzed in real-time. According to particular examples, the digital sampling rates are adaptively chosen based on the neurophysiological and neurological data being measured.

In one particular example, the content selection and meta-tagging system includes EEG 111 measurements made using scalp level electrodes, EOG 113 measurements made using shielded electrodes to track eye data, GSR 115 measurements performed using a differential measurement system, a facial muscular measurement through shielded electrodes placed at specific locations on the face, and a facial affect graphic and video analyzer adaptively derived for each individual.

In particular examples, the data collection devices are clock synchronized with a stimulus presentation device 101. In particular examples, the data collection devices 105 also include a condition evaluation subsystem that provides auto triggers, alerts and status monitoring and visualization components that continuously monitor the status of the subject, data being collected, and the data collection instruments. The condition evaluation subsystem may also present visual alerts and automatically trigger remedial actions. According to various examples, the data collection devices include mechanisms for not only monitoring subject neuro-response to stimulus materials, but also include mechanisms for identifying and monitoring the stimulus materials. For example, data collection devices 105 may be synchronized with a set-top box to monitor channel changes. In other examples, data collection devices 105 may be directionally synchronized to monitor when a subject is no longer paying attention to stimulus material. In still other examples, the data collection devices 105 may receive and store stimulus material generally being viewed by the subject, whether the stimulus is a program, a commercial, printed material, or a scene outside a window. The data collected allows analysis of neuro-response information and correlation of the information to actual stimulus material and not mere subject distractions.

According to various examples, the content selection and meta-tagging system also includes a data cleanser device 121. In particular examples, the data cleanser device 121 filters the collected data to remove noise, artifacts, and other irrelevant data using fixed and adaptive filtering, weighted averaging, advanced component extraction (like PCA, ICA), vector and component separation methods, etc. This device cleanses the data by removing both exogenous noise (where the source is outside the physiology of the subject, e.g. a phone ringing while a subject is viewing a video) and endogenous artifacts (where the source could be neurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectively isolate and review the response data and identify epochs with time domain and/or frequency domain attributes that correspond to artifacts such as line frequency, eye blinks, and muscle movements. The artifact removal subsystem then cleanses the artifacts by either omitting these epochs, or by replacing these epoch data with an estimate based on the other clean data (for example, an EEG nearest neighbor weighted averaging approach).

According to various examples, the data cleanser device 121 is implemented using hardware, firmware, and/or software. It should be noted that although a data cleanser device 121 is shown located after a data collection device 105 and before data analyzer 181, the data cleanser device 121 like other components may have a location and functionality that varies based on system implementation. For example, some systems may not use any automated data cleanser device whatsoever while in other systems, data cleanser devices may be integrated into individual data collection devices.

According to various examples, an optional data meta attributes repository 131 provides information on the stimulus material being presented. According to various examples, stimulus attributes include properties of the stimulus materials as well as purposes, presentation attributes, report generation attributes, etc. In particular examples, stimulus attributes include time span, channel, rating, media, type, etc. Stimulus attributes may also include positions of entities in various frames, object relationships, locations of objects and duration of display. Purpose attributes include aspiration and objects of the stimulus including excitement, memory retention, associations, etc. Presentation attributes include audio, video, imagery, and messages needed for enhancement or avoidance. Other attributes may or may not also be included in the stimulus attributes repository or some other repository.

The data cleanser device 121 and the stimulus attributes repository 131 pass data to the data analyzer 181. The data analyzer 181 uses a variety of mechanisms to analyze underlying data in the system to determine resonance. According to various examples, the data analyzer customizes and extracts the independent neurological and neuro-physiological parameters for each individual in each modality, and blends the estimates within a modality as well as across modalities to elicit an enhanced response to the presented stimulus material. In particular examples, the data analyzer 181 aggregates the response measures across subjects in a dataset.

According to various examples, neurological and neuro-physiological signatures are measured using time domain analyses and frequency domain analyses. Such analyses use parameters that are common across individuals as well as parameters that are unique to each individual. The analyses could also include statistical parameter extraction and fuzzy logic based attribute estimation from both the time and frequency components of the synthesized response.

In some examples, statistical parameters used in a blended effectiveness estimate include evaluations of skew, peaks, first and second moments, population distribution, as well as fuzzy estimates of attention, emotional engagement and memory retention responses.

According to various examples, the data analyzer 181 may include an intra-modality response synthesizer and a cross-modality response synthesizer. In particular examples, the intra-modality response synthesizer is configured to customize and extract the independent neurological and neurophysiological parameters for each individual in each modality and blend the estimates within a modality analytically to elicit an enhanced response to the presented stimuli. In particular examples, the intra-modality response synthesizer also aggregates data from different subjects in a dataset.

According to various examples, the cross-modality response synthesizer or fusion device blends different intra-modality responses, including raw signals and signals output. The combination of signals enhances the measures of effectiveness within a modality. The cross-modality response fusion device can also aggregate data from different subjects in a dataset.

According to various examples, the data analyzer 181 also includes a composite enhanced effectiveness estimator (CEEE) that combines the enhanced responses and estimates from each modality to provide a blended estimate of the effectiveness. In particular examples, blended estimates are provided for each exposure of a subject to stimulus materials. The blended estimates are evaluated over time to assess resonance characteristics. According to various examples, numerical values are assigned to each blended estimate. The numerical values may correspond to the intensity of neuro-response measurements, the significance of peaks, the change between peaks, etc. Higher numerical values may correspond to higher significance in neuro-response intensity. Lower numerical values may correspond to lower significance or even insignificant neuro-response activity. In other examples, multiple values are assigned to each blended estimate. In still other examples, blended estimates of neuro-response significance are graphically represented to show changes after repeated exposure.

According to various examples, the data analyzer 181 provides analyzed and enhanced response data to a data communication device. It should be noted that in particular instances, a data communication device is not necessary. According to various examples, the data communication device provides raw and/or analyzed data and insights. In particular examples, the data communication device may include mechanisms for the compression and encryption of data for secure storage and communication.

According to various examples, the data communication device transmits data using protocols such as the File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP) along with a variety of conventional, bus, wired network, wireless network, satellite, and proprietary communication protocols. The data transmitted can include the data in its entirety, excerpts of data, converted data, and/or elicited response measures. According to various examples, the data communication device is a set top box, wireless device, computer system, etc. that transmits data obtained from a data collection device to a resonance estimator 185. In particular examples, the data communication device may transmit data even before data cleansing or data analysis. In other examples, the data communication device may transmit data after data cleansing and analysis.

In particular examples, the data communication device sends data to a resonance estimator 185. According to various examples, the resonance estimator 185 assesses and extracts resonance patterns. In particular examples, the resonance estimator 185 determines entity positions in various stimulus segments and matches position information with eye tracking paths while correlating saccades with neural assessments of attention, memory retention, and emotional engagement. In particular examples, the resonance estimator 185 also collects and integrates user behavioral and survey responses with the analyzed response data to more effectively estimate resonance.

According to various examples, the resonance estimator 185 provides data to a priming repository system 187. In particular examples, the priming repository system 187 associates meta-tags with various temporal and spatial locations in stimulus material, such as a television program, movie, video, audio program, print advertisement, etc. In some examples, every second of a show is associated with a set of meta-tags. In other examples, commercial or advertisement (ad) breaks are provided with a set of meta-tags that identify commercial or advertising content that would be most suitable for a particular break.

Pre-break content may identify categories of products and services that are primed at a particular point in a program. The content may also specify the level of priming associated with each category of product or service. For example, a movie may show old house and buildings. Meta-tags may be manually or automatically generated to indicate that commercials for home improvement products would be suitable for a particular advertisement break.

In some instances, meta-tags may include spatial and temporal information indicating where and when particular advertisements should be placed. For example, a documentary about wildlife that shows a blank wall in several scenes may include meta-tags that indicate a banner advertisement for nature oriented vacations may be suitable. The advertisements may be separate from a program or integrated into a program. According to various examples, the priming repository system 187 also identifies scenes eliciting significant audience resonance to particular products and services as well as the level and intensity of resonance.

A variety of data can be stored for later analysis, management, manipulation, and retrieval. In particular examples, the repository could be used for tracking stimulus attributes and presentation attributes, audience responses optionally could also be integrated into meta-tags.

As with a variety of the components in the system, the repository can be co-located with the rest of the system and the user, or could be implemented in a remote location. It could also be optionally separated into repository system that could be centralized or distributed at the provider or providers of the stimulus material. In other examples, the repository system itself is integrated into a library of stimulus materials such as a media library.

FIG. 2 illustrates examples of data models that may be provided with a stimulus attributes repository. According to various examples, a stimulus attributes data model 201 includes a channel 203, media type 205, time span 207, audience 209, and demographic information 211. A stimulus purpose data model 215 may include intents 217 and objectives 219. According to various examples, stimulus attributes data model 201 also includes spatial and temporal information 221 about entities and emerging relationships between entities.

According to various examples, another stimulus attributes data model 221 includes creation attributes 223, ownership attributes 225, broadcast attributes 227, and statistical, demographic and/or survey based identifiers 229 for automatically integrating the neuro-physiological and neuro-behavioral response with other attributes and meta-information associated with the stimulus.

According to various examples, a stimulus priming data model 231 includes fields for identifying advertisement breaks 233 and scenes 235 that can be associated with various priming levels 237 and audience resonance measurements 239. In particular examples, the data model 231 provides temporal and spatial information for ads, scenes, events, locations, etc. that may be associated with priming levels and audience resonance measurements. In some examples, priming levels for a variety of products, services, offerings, etc. are correlated with temporal and spatial information in source material such as a movie, billboard, advertisement, commercial, store shelf, etc. In some examples, the data model associates with each second of a show a set of meta-tags for pre-break content indicating categories of products and services that are primed. The level of priming associated with each category of product or service when the advertisement break is specified may also be provided. Audience resonance measurements and maximal audience resonance measurements for various scenes and advertisement breaks may be maintained and correlated with sets of products, services, offerings, etc.

The priming and resonance information may be used to select stimulus content suited for particular levels of priming and resonance. In some examples, the priming and resonance information may be used to more intelligently price advertising breaks based on value to advertisers.

FIG. 3 illustrates examples of data models that can be used for storage of information associated with tracking and measurement of resonance. According to various examples, a dataset data model 301 includes an experiment name 303 and/or identifier, client attributes 305, a subject pool 307, logistics information 309 such as the location, date, and time of testing, and stimulus material 311 including stimulus material attributes.

In particular examples, a subject attribute data model 315 includes a subject name 317 and/or identifier, contact information 321, and demographic attributes 319 that may be useful for review of neurological and neuro-physiological data. Some examples of pertinent demographic attributes include marriage status, employment status, occupation, household income, household size and composition, ethnicity, geographic location, sex, race. Other fields that may be included in data model 315 include shopping preferences, entertainment preferences, and financial preferences. Shopping preferences include favorite stores, shopping frequency, categories shopped, favorite brands. Entertainment preferences include network/cable/satellite access capabilities, favorite shows, favorite genres, and favorite actors. Financial preferences include favorite insurance companies, preferred investment practices, banking preferences, and favorite online financial instruments. A variety of subject attributes may be included in a subject attributes data model 315 and data models may be preset or custom generated to suit particular purposes.

According to various examples, data models for neuro-feedback association 325 identify experimental protocols 327, modalities included 329 such as EEG, EOG, GSR, surveys conducted, and experiment design parameters 333 such as segments and segment attributes. Other fields may include experiment presentation scripts, segment length, segment details like stimulus material used, inter-subject variations, intra-subject variations, instructions, presentation order, survey questions used, etc. Other data models may include a data collection data model 337. According to various examples, the data collection data model 337 includes recording attributes 339 such as station and location identifiers, the data and time of recording, and operator details. In particular examples, equipment attributes 341 include an amplifier identifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes like EEG cap layout, active channels, sampling frequency, and filters used. EOG specific attributes include the number and type of sensors used, location of sensors applied, etc. Eye tracking specific attributes include the type of tracker used, data recording frequency, data being recorded, recording format, etc. According to various examples, data storage attributes 345 include file storage conventions (format, naming convention, dating convention), storage location, archival attributes, expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/or identifier, an accessed data collection 353 such as data segments involved (models, databases/cubes, tables, etc.), access security attributes 355 included who has what type of access, and refresh attributes 357 such as the expiry of the query, refresh frequency, etc. Other fields such as push-pull preferences can also be included to identify an auto push reporting driver or a user driven report retrieval system.

FIG. 4 illustrates examples of queries that can be performed to obtain data associated with content selection and meta-tagging. According to various examples, queries are defined from general or customized scripting languages and constructs, visual mechanisms, a library of preset queries, diagnostic querying including drill-down diagnostics, and eliciting what if scenarios. According to various examples, subject attributes queries 415 may be configured to obtain data from a neuro-informatics repository using a location 417 or geographic information, session information 421 such as testing times and dates, and demographic attributes 419. Demographics attributes include household income, household size and status, education level, age of kids, etc.

Other queries may retrieve stimulus material based on shopping preferences of subject participants, countenance, physiological assessment, completion status. For example, a user may query for data associated with product categories, products shopped, shops frequented, subject eye correction status, color blindness, subject state, signal strength of measured responses, alpha frequency band ringers, muscle movement assessments, segments completed, etc. Experimental design based queries may obtain data from a neuro-informatics repository based on experiment protocols 427, product category 429, surveys included 431, and stimulus provided 433. Other fields that may be used include the number of protocol repetitions used, combination of protocols used, and usage configuration of surveys.

Client and industry based queries may obtain data based on the types of industries included in testing, specific categories tested, client companies involved, and brands being tested. Response assessment based queries 437 may include attention scores 439, emotion scores, 441, retention scores 443, and effectiveness scores 445. Such queries may obtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds, variance measures, number of peaks detected, etc. Group response queries may include group statistics like mean, variance, kurtosis, p-value, etc., group size, and outlier assessment measures. Still other queries may involve testing attributes like test location, time period, test repetition count, test station, and test operator fields. A variety of types and combinations of types of queries can be used to efficiently extract data.

FIG. 5 illustrates examples of reports that can be generated. According to various examples, client assessment summary reports 501 include effectiveness measures 503, component assessment measures 505, and resonance measures 507. Effectiveness assessment measures include composite assessment measure(s), industry/category/client specific placement (percentile, ranking, etc.), actionable grouping assessment such as removing material, modifying segments, or fine tuning specific elements, etc, and the evolution of the effectiveness profile over time. In particular examples, component assessment reports include component assessment measures like attention, emotional engagement scores, percentile placement, ranking, etc. Component profile measures include time based evolution of the component measures and profile statistical assessments. According to various examples, reports include the number of times material is assessed, attributes of the multiple presentations used, evolution of the response assessment measures over the multiple presentations, and usage recommendations.

According to various examples, client cumulative reports 511 include media grouped reporting 513 of all stimulus assessed, campaign grouped reporting 515 of stimulus assessed, and time/location grouped reporting 517 of stimulus assessed. According to various examples, industry cumulative and syndicated reports 521 include aggregate assessment responses measures 523, top performer lists 525, bottom performer lists 527, outliers 529, and trend reporting 531. In particular examples, tracking and reporting includes specific products, categories, companies, brands.

FIG. 6 illustrates one example of content selection and meta-tagging. At 601, stimulus material is provided to multiple subjects. According to various examples, stimulus includes streaming video and audio. In particular examples, subjects view stimulus in their own homes in group or individual settings. In some examples, verbal and written responses are collected for use without neuro-response measurements. In other examples, verbal and written responses are correlated with neuro-response measurements. At 603, subject neuro-response measurements are collected using a variety of modalities, such as EEG, ERP, EOG, GSR, etc. At 605, data is passed through a data cleanser to remove noise and artifacts that may make data more difficult to interpret. According to various examples, the data cleanser removes EEG electrical activity associated with blinking and other endogenous/exogenous artifacts.

According to various examples, data analysis is performed. Data analysis may include intra-modality response synthesis and cross-modality response synthesis to enhance effectiveness measures. It should be noted that in some particular instances, one type of synthesis may be performed without performing other types of synthesis. For example, cross-modality response synthesis may be performed with or without intra-modality synthesis.

A variety of mechanisms can be used to perform data analysis. In particular examples, a stimulus attributes repository 131 is accessed to obtain attributes and characteristics of the stimulus materials, along with purposes, intents, objectives, etc. In particular examples, EEG response data is synthesized to provide an enhanced assessment of effectiveness. According to various examples, EEG measures electrical activity resulting from thousands of simultaneous neural processes associated with different portions of the brain. EEG data can be classified in various bands. According to various examples, brainwave frequencies include delta, theta, alpha, beta, and gamma frequency ranges. Delta waves are classified as those less than 4 Hz and are prominent during deep sleep. Theta waves have frequencies between 3.5 to 7.5 Hz and are associated with memories, attention, emotions, and sensations. Theta waves are typically prominent during states of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around 10 Hz. Alpha waves are prominent during states of relaxation. Beta waves have a frequency range between 14 and 30 Hz. Beta waves are prominent during states of motor control, long range synchronization between brain areas, analytical problem solving, judgment, and decision making. Gamma waves occur between 30 and 60 Hz and are involved in binding of different populations of neurons together into a network for the purpose of carrying out a certain cognitive or motor function, as well as in attention and memory. Because the skull and dermal layers attenuate waves in this frequency range, brain waves above 75-80 Hz are difficult to detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosure recognize that analyzing high gamma band (kappa-band: Above 60 Hz) measurements, in addition to theta, alpha, beta, and low gamma band measurements, enhances neurological attention, emotional engagement and retention component estimates. In particular examples, EEG measurements including difficult to detect high gamma or kappa band measurements are obtained, enhanced, and evaluated. Subject and task specific signature sub-bands in the theta, alpha, beta, gamma and kappa bands are identified to provide enhanced response estimates. According to various examples, high gamma waves (kappa-band) above 80 Hz (typically detectable with sub-cranial EEG and/or magnetoencephalograophy) can be used in inverse model-based enhancement of the frequency responses to the stimuli.

Various examples of the present disclosure recognize that particular sub-bands within each frequency range have particular prominence during certain activities. A subset of the frequencies in a particular band is referred to herein as a sub-band. For example, a sub-band may include the 40-45 Hz range within the gamma band. In particular examples, multiple sub-bands within the different bands are selected while remaining frequencies are band pass filtered. In particular examples, multiple sub-band responses may be enhanced, while the remaining frequency responses may be attenuated.

An information theory based band-weighting model is used for adaptive extraction of selective dataset specific, subject specific, task specific bands to enhance the effectiveness measure. Adaptive extraction may be performed using fuzzy scaling. Stimuli can be presented and enhanced measurements determined multiple times to determine the variation profiles across multiple presentations. Determining various profiles provides an enhanced assessment of the primary responses as well as the longevity (wear-out) of the marketing and entertainment stimuli. The synchronous response of multiple individuals to stimuli presented in concert is measured to determine an enhanced across subject synchrony measure of effectiveness. According to various examples, the synchronous response may be determined for multiple subjects residing in separate locations or for multiple subjects residing in the same location.

Although a variety of synthesis mechanisms are described, it should be recognized that any number of mechanisms can be applied—in sequence or in parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhanced significance data, additional cross-modality synthesis mechanisms can also be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR, EOG, and facial emotion encoding are connected to a cross-modality synthesis mechanism. Other mechanisms as well as variations and enhancements on existing mechanisms may also be included. According to various examples, data from a specific modality can be enhanced using data from one or more other modalities. In particular examples, EEG typically makes frequency measurements in different bands like alpha, beta and gamma to provide estimates of significance. However, the techniques of the present disclosure recognize that significance measures can be enhanced further using information from other modalities.

For example, facial emotion encoding measures can be used to enhance the valence of the EEG emotional engagement measure. EOG and eye tracking saccadic measures of object entities can be used to enhance the EEG estimates of significance including but not limited to attention, emotional engagement, and memory retention. According to various examples, a cross-modality synthesis mechanism performs time and phase shifting of data to allow data from different modalities to align. In some examples, it is recognized that an EEG response will often occur hundreds of milliseconds before a facial emotion measurement changes. Correlations can be drawn and time and phase shifts made on an individual as well as a group basis. In other examples, saccadic eye movements may be determined as occurring before and after particular EEG responses. According to various examples, time corrected GSR measures are used to scale and enhance the EEG estimates of significance including attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domain difference event-related potential components (like the DERP) in specific regions correlates with subject responsiveness to specific stimulus. According to various examples, ERP measures are enhanced using EEG time-frequency measures (ERPSP) in response to the presentation of the marketing and entertainment stimuli. Specific portions are extracted and isolated to identify ERP, DERP and ERPSP analyses to perform. In particular examples, an EEG frequency estimation of attention, emotion and memory retention (ERPSP) is used as a co-factor in enhancing the ERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specific objects of stimulus. Eye tracking measures the subject's gaze path, location and dwell on specific objects of stimulus. According to various examples, EOG and eye tracking is enhanced by measuring the presence of lambda waves (a neurophysiological index of saccade effectiveness) in the ongoing EEG in the occipital and extra striate regions, triggered by the slope of saccade-onset to estimate the significance of the EOG and eye tracking measures. In particular examples, specific EEG signatures of activity such as slow potential shifts and measures of coherence in time-frequency responses at the Frontal Eye Field (FEF) regions that preceded saccade-onset are measured to enhance the effectiveness of the saccadic activity data.

GSR typically measures the change in general arousal in response to stimulus presented. According to various examples, GSR is enhanced by correlating EEG/ERP responses and the GSR measurement to get an enhanced estimate of subject engagement. The GSR latency baselines are used in constructing a time-corrected GSR response to the stimulus. The time-corrected GSR response is co-factored with the EEG measures to enhance GSR significance measures.

According to various examples, facial emotion encoding uses templates generated by measuring facial muscle positions and movements of individuals expressing various emotions prior to the testing session. These individual specific facial emotion encoding templates are matched with the individual responses to identify subject emotional response. In particular examples, these facial emotion encoding measurements are enhanced by evaluating inter-hemispherical asymmetries in EEG responses in specific frequency bands and measuring frequency band interactions. The techniques of the present disclosure recognize that not only are particular frequency bands significant in EEG responses, but particular frequency bands used for communication between particular areas of the brain are significant. Consequently, these EEG responses enhance the EMG, graphic and video based facial emotion identification.

According to various examples, post-stimulus versus pre-stimulus differential measurements of ERP time domain components in multiple regions of the brain (DERP) are measured at 607. The differential measures give a mechanism for eliciting responses attributable to the stimulus. For example the messaging response attributable to an advertisement or the brand response attributable to multiple brands is determined using pre-resonance and post-resonance estimates

At 609, target versus distracter stimulus differential responses are determined for different regions of the brain (DERP). At 611, event related time-frequency analysis of the differential response (DERPSPs) are used to assess the attention, emotion and memory retention measures across multiple frequency bands. According to various examples, the multiple frequency bands include theta, alpha, beta, gamma and high gamma or kappa. At 613, priming levels and resonance for various products, services, and offerings are determined at different locations in the stimulus material. In some examples, priming levels and resonance are manually determined. In other examples, priming levels and resonance are automatically determined using neuro-response measurements. According to various examples, video streams are modified with different inserted advertisements for various products and services to determine the effectiveness of the inserted advertisements based on priming levels and resonance of the source material.

At 617, multiple trials are performed to enhance priming and resonance measures. In some examples, stimulus. In some examples, multiple trials are performed to enhance resonance measures.

In particular examples, the priming and resonance measures are sent to a priming repository 619. The priming repository 619 may be used to automatically select advertising suited for particular ad breaks.

FIG. 7 illustrates an example of a technique for estimating resonance. According to various examples, measurements from different modalities are obtained. According to various examples, measurements including Differential Event Related Potential (DERP), Differential Event Related Power Spectral Perturbations (DERPSPs), Pupilary Response, etc., are blended to obtain a combined measurement. In particular examples, each measurement may have to be aligned appropriately in order to allow blending. According to various examples, a resonance estimator includes mechanisms to use and blend different measures from across the modalities from the data analyzer. In particular examples, the data includes the DERP measures, DERPSPs, pupilary response, GSR, eye movement, coherence, coupling and lambda wave based response. Measurements across modalities are blended to elicit a synthesized measure of user resonance.

In particular examples, user resonance to attributes of stimulus material such as communication, concept, experience, message, images, genre, product categories, service categories, etc. are measured at 701. The attributes of the stimulus material are evaluated to identify ad categories and genres that are naturally primed as a consequence of the preceding content 703. The effectiveness of source material may be determined using a mechanism to weigh and combine the outputs of the data analyzer. According to various examples, a set of predetermined weights and nonlinear functions combine the outputs of the data analyzer to determine a hierarchy of the effectiveness of a set of categories for products and services that are primed by the source material or pre-break show content at 705. According to various examples, a set of predetermined weights and nonlinear functions combine the outputs of the data analyzer to determine scenes of a show of maximal effectiveness to perform a differential extraction of categories of products and services that are effectively primed by the source material at 707.

At 711, priming levels for various products and services are correlated with various ad breaks based on the number of scenes of maximal effectiveness, the number of categories of products and services primed by the pre break content, and the level of priming effectiveness for each category.

At 713, priming levels and resonance are maintained in a priming level repository. In some examples, the priming levels and resonance are written to the source material.

According to various examples, various mechanisms such as the data collection mechanisms, the intra-modality synthesis mechanisms, cross-modality synthesis mechanisms, etc. are implemented on multiple devices. However, it is also possible that the various mechanisms be implemented in hardware, firmware, and/or software in a single system. FIG. 8 provides one example of a system that can be used to implement one or more mechanisms. For example, the system shown in FIG. 8 may be used to implement a resonance measurement system.

According to particular examples, a system 800 suitable for implementing particular examples of the present disclosure includes a processor 801, a memory 803, an interface 811, and a bus 815 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the processor 801 is responsible for such tasks such as pattern generation. Various specially configured devices can also be used in place of a processor 801 or in addition to processor 801. The complete implementation can also be done in custom hardware. The interface 811 is typically configured to send and receive data packets or data segments over a network. Particular examples of interfaces the device supports include host bus adapter (HBA) interfaces, Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such as fast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as data synthesis.

According to particular examples, the system 800 uses memory 803 to store data, algorithms and program instructions. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store received data and process received data.

Because such information and program instructions may be employed to implement the systems/methods described herein, the present disclosure relates to tangible, machine readable media that include program instructions, state information, etc. for performing various operations described herein. Examples of machine-readable media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks and DVDs; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and random access memory (RAM). Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. Therefore, the present examples are to be considered as illustrative and not restrictive and the disclosure is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims. 

1. (canceled)
 2. A method comprising: analyzing first neuro-response data gathered from a first subject exposed to source material to determine a first subject resonance to a first portion of the source material and a second subject resonance to a second portion of the source material; identifying a first priming level of the first portion of the source material based on a first attribute of the source material and the first subject resonance; and identifying a second priming level of the second portion of the source material based on a second attribute of the source material and the second subject resonance; and selecting (a) the first portion of the source material or (b) the second portion of the source material for inclusion of an advertisement based on the first priming level and the second priming level.
 3. The method of claim 2 further comprising analyzing second neuro-response data gathered from at least one of the first subject or a second subject exposed to the advertisement to determine a third subject resonance to the advertisement, wherein selecting (a) the first portion of the source material or (b) the second portion of the source material for inclusion of the advertisement further based on the third resonance.
 4. The method of claim 2 further comprising appraising a financial value of the first portion of the source material based on the first priming level.
 5. The method of claim 2 further comprising appraising a financial value of the first of the source material and the second portion of the source material based on the first priming level and the second priming level.
 6. The method of claim 2 wherein the first portion of the source material and the second portion of the source material are temporally separate.
 7. The method of claim 2, wherein the first portion of the source material is temporally concurrent with the second portion of the source material, and the first portion of the source material is spaced a distance from the second portion of the source material.
 8. The method of claim 2, wherein the first neuro-response data comprises two frequency bands of electroencephalographic data and the first subject resonance is based on a coherence between the two frequency bands.
 9. The method of claim 2 further comprising tagging the source material with the first subject resonance, the second subject resonance, the first priming level and the second priming level.
 10. The method of claim 2, wherein the advertisement is a product placement within a scene of the source material.
 11. A system comprising: an analyzer to analyze first neuro-response data gathered from a first subject exposed to source material to determine a first subject resonance to a first portion of the source material and a second subject resonance to a second portion of the source material; a processor to identify a first priming level of the first portion of the source material based on a first attribute of the source material and the first subject resonance and to identify a second priming level of the second portion of the source material based on a second attribute of the source material and the second subject resonance; and a selector to select (a) the first portion of the source material or (b) the second portion of the source material for inclusion of an advertisement based on the first priming level or the second priming level.
 12. The system of claim 11, wherein the analyzer is to analyze second neuro-response data gathered from the first subject or a second subject exposed to the advertisement to determine a third subject resonance to the advertisement, and the selector is to select (a) the first portion of the source material or (b) the second portion of the source material for inclusion of the advertisement based on the first priming level, the second priming level and the third resonance.
 13. The system of claim 11 further comprising an appraiser to determine a financial value of the first portion of the source material and the second portion of the source material based on the first priming level and the second priming level.
 14. The system of claim 11, wherein the first portion of the source material and the second portion of the source material are temporally separate.
 15. The system of claim 11, wherein the first portion of the source material is temporally concurrent with the second portion of the source material, and the first portion of the source material is spaced a distance from the second portion of the source material.
 16. A tangible machine readable storage device or storage disc comprising instructions, which when read, cause a machine to at least: analyze first neuro-response data gathered from a first subject exposed to source material to determine a first subject resonance to a first portion of the source material and a second subject resonance to a second portion of the source material; identify a first priming level of the first portion of the source material based on a first attribute of the source material and the first subject resonance; identify a second priming level of the second portion of the source material based on a second attribute of the source material and the second subject resonance; and select (a) the first portion of the source material or (b) the second portion of the source material for inclusion of an advertisement based on the at least one of first priming level and the second priming level.
 17. The storage device or storage disc of claim 16, wherein the instructions further cause the machine to: analyze second neuro-response data gathered from at least one of the first subject or a second subject exposed to the advertisement to determine a third subject resonance to the advertisement; select (a) the first portion of the source material or (b) the second portion of the source material for inclusion of the advertisement further based on the third resonance.
 18. The storage device or storage disc of claim 16, wherein the instructions further cause the machine to appraise a financial value of the first portion of the source material and the second portion of the source material based on the first priming level and the second priming level.
 19. The storage device or storage disc of claim 16, wherein the first portion of the source material and the second portion of the source material are temporally separate.
 20. The storage device or storage disc of claim 16, wherein the first portion of the source material and the second portion of the source material are temporally concurrent, and the first portion of the source material is spaced a distance from the second portion of the source material.
 21. The storage device or storage disc of claim 16, wherein the first neuro-response data comprises two frequency bands of electroencephalographic data and the first subject resonance is based on a coherence between the two frequency bands. 